Add Gradio app for DCGAN chess piece generator

README.md

@@ -1,13 +1,24 @@
 ---
-title: Chessman Image-DCGAN
-emoji: 🐠
-colorFrom: purple
-colorTo: gray
+title: Chessman DCGAN Generator
+emoji: ♟️
+colorFrom: blue
+colorTo: purple
 sdk: gradio
 sdk_version: 6.5.1
 app_file: app.py
 pinned: false
 license: mit
+tags:
+- gan
+- dcgan
+- image-generation
+- chess
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ♟️ Chessman DCGAN Generator
+
+Generate unique AI-created chess piece images using a Deep Convolutional GAN!
+
+This Space uses a DCGAN model trained on the Chessman Image Dataset to generate 64×64 RGB images of chess pieces.
+
+**Model**: [manupawar6388/Chessman_image-DCGAN](https://huggingface.co/manupawar6388/Chessman_image-DCGAN)

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+# Download and load the model
+model_path = hf_hub_download(
+    repo_id="manupawar6388/Chessman_image-DCGAN",
+    filename="dcgan_generator.keras"
+)
+generator = tf.keras.models.load_model(model_path)
+
+def generate_chess_piece():
+    """Generate a random chess piece image"""
+    # Generate random noise
+    noise = tf.random.normal([1, 100])
+    
+    # Generate image
+    generated_image = generator(noise, training=False)
+    
+    # Rescale to [0, 255]
+    img_array = ((generated_image[0, :, :, :] * 127.5) + 127.5).numpy().astype("uint8")
+    
+    # Convert to PIL Image
+    img = Image.fromarray(img_array)
+    
+    return img
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # ♟️ Chessman DCGAN Generator
+        
+        Generate unique AI-created chess piece images using a Deep Convolutional GAN trained on the Chessman Image Dataset.
+        
+        **Model**: DCGAN (50 epochs) | **Output**: 64×64 RGB images
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            generate_btn = gr.Button("🎲 Generate Chess Piece", variant="primary", size="lg")
+        
+        with gr.Column():
+            output_image = gr.Image(label="Generated Chess Piece", type="pil")
+    
+    gr.Markdown(
+        """
+        ### About
+        This model uses a Deep Convolutional Generative Adversarial Network (DCGAN) to generate chess piece images.
+        Each generation is unique, created from random noise.
+        
+        **Dataset**: [Chessman Image Dataset](https://www.kaggle.com/datasets/niteshfre/chessman-image-dataset) (552 images)
+        
+        **Model Repository**: [manupawar6388/Chessman_image-DCGAN](https://huggingface.co/manupawar6388/Chessman_image-DCGAN)
+        """
+    )
+    
+    # Event handler
+    generate_btn.click(fn=generate_chess_piece, outputs=output_image)
+    
+    # Generate on load
+    demo.load(fn=generate_chess_piece, outputs=output_image)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow>=2.10.0
+gradio>=4.0.0
+huggingface-hub>=0.20.0
+pillow>=9.0.0
+numpy>=1.23.0